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Managers and employees alike have gotten the message: AI is part of their job, and it\u2019s time to embrace it. That\u2019s the good news. The bad news is that, even as AI adoption is supposed to create efficiency, it can also do the opposite as dozens of teams and individuals stand up AI initiatives that are never finished or that serve no strategic purpose.<\/p>\n
\u201cAn executive knows there are three things that will move the needle for their business\u2014not 300 things. But if you ask everyone how many use cases they have, they all have 300\u2014but they\u2019re not all equally important,\u201d said Brett Greenstein, chief AI officer at consultancy firm West Monroe.<\/p>\n
Greenstein made the remark at Fortune Brainstorm Tech in Aspen earlier this month as part of a roundtable that examined the benefits and pitfalls of rapid AI adoption across different corporate sectors.<\/p>\n
Sean Bruich, SVP and CTO\u00a0of pharmaceutical giant Amgen, described himself as \u201ca card-carrying data scientist\u201d and is deeply familiar with AI. He made the case that some companies may need to focus less on the technical dimensions of AI, and more on the business and political challenges of implementing these new tools.<\/p>\n
\u201cThe explosion of pilots around AI inside a company can become an incredible drag on your ability to move quickly, because each one of those has a champion and a team and a set of KPIs and a data engineering squad,\u201d said Bruich, who added that too many pilots can obscure which elements of AI are delivering business value, and that some firms are too slow to kill the ones that don\u2019t.<\/p>\n
Dan Gill related that he has learned similar lessons as chief product officer at Carvana, a logistics-heavy firm that arranges vehicle purchases between millions of buyers and sellers, and runs a massive financing operation.<\/p>\n
\u201cGetting one thing all the way done is much more valuable than five things progressed to 20% each,\u201d he observed. \u201c[AI means] prototyping is cheap, and documentation is cheap, and code generation is cheap, and suddenly it\u2019s so easy to do things\u2014but the reality is getting that number one thing all the way done and iterating on it really tightly is much, much, much more valuable than just advancing lots of stuff.\u201d<\/p>\n
Even as AI offers tantalizing promises of massive productivity gains, the reality is that too many companies are expending valuable resources on projects that lower productivity because they don\u2019t accomplish anything useful. The solution, said Nizar Trigui, chief technology officer at logistics giant GXO, is to ensure company leaders focus as much on the business dimensions of AI as they do teaching the tech angles.<\/p>\n
\u201cWe all found what we\u2019re doing is no longer just a technology-led transformation. This has to be a business-led transformation\u2014it has to be led from the top. It has to be from the board. It has to be from the CEO\u2014the entire executive team. And they need to be engaged in that entire transformation,\u201d he said.<\/p>\n
This is easier said than done, noted the panelists, because there is a tension between adopting AI efficiently and the metrics that have historically signaled success\u2014headcount size, spreadsheets, KPIs, and so on. Despite these challenges, though, leaders no longer have the option to implement AI without a comprehensive strategy.<\/p>\n
\u201cI think we\u2019ve crossed the Rubicon \u2026 The ships are burnt,\u201d said Gill. \u201cThere is no going back, but the future is bright.\u201d<\/p>\n<\/div>\n
#Drowning #Companies #launching #hundreds #projects #problem<\/p>\n","protected":false},"excerpt":{"rendered":"
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